Search

Background: Resistant infectious diseases caused by gram-negative bacteria are among the most serious worldwide health problems. Antimicrobial peptides (AMPs) have been explored as promising antibacterial, antibiofilm, and anti-infective candidates to address these health challenges., Major Conclusions: Here we report the potent antibacterial effect of the peptide PaDBS1R6 on clinical bacterial isolates and identify an immunomodulatory peptide fragment incorporated within it. PaDBS1R6 was evaluated against Acinetobacter baumannii and Escherichia coli clinical isolates and had minimal inhibitory concentration (MIC) values from 8 to 32 μmol L -1 . It had a rapid bactericidal effect, with eradication showing within 3 min of incubation, depending on the bacterial strain tested. In addition, PaDBS1R6 inhibited biofilm formation for A. baumannii and E. coli and was non-toxic toward healthy mammalian cells. These findings are explained by the preference of PaDBS1R6 for anionic membranes over neutral membranes, as assessed by surface plasmon resonance assays and molecular dynamics simulations. Considering its potent antibacterial activity, PaDBS1R6 was used as a template for sliding-window fr agmentation studies (window size = 10 residues). Among the sliding-window fragments, PaDBS1R6F8, PaDBS1R6F9, and PaDBS1R6F10 were ineffective against any of the bacterial strains tested. Additional biological assays were conducted, including nitric oxide (NO) modulation and wound scratch assays, and the R6F8 peptide fragment was found to be active in modulating NO levels, as well as having strong wound healing properties., General Significance: This study proposes a new concept whereby peptides with different biological properties can be derived by the screening of fragments from within potent AMPs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Antimicrobial peptides (AMP) represent an alternative in the treatment of fungal infections associated with countless deaths. Here, we report a new AMP, named KWI-19, which was designed based on a peptide encrypted in the sequence of an Inga laurina Kunitz-type inhibitor (ILTI). KWI-19 inhibited the growth of Candida species and acted as a fungicidal agent from 2.5 to 20 μmol L -1 , also showing synergistic activity with amphotericin B. Kinetic assays showed that KWI-19 killed Candida tropicalis cells within 60 min. We also report the membrane-associated mechanisms of action of KWI-19 and its interaction with ergosterol. KWI-19 was also characterized as a potent antibiofilm peptide, with activity against C. tropicalis. Finally, non-toxicity was reported against Galleria mellonella larvae, thus strengthening the interest in all the bioactivities mentioned above. This study extends our knowledge on how AMPs can be engineered from peptides encrypted in larger proteins and their potential as candicidal agents., Competing Interests: Declaration of competing interest The authors declare no competing interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Computer-aided identification and design tools are indispensable for developing antimicrobial agents for controlling antibiotic-resistant bacteria. Antimicrobial peptides (AMPs) have aroused intense interest, since they have a broad spectrum of activity, and therefore, several systems for predicting antimicrobial peptides have been developed, using scalar physicochemical properties; however, regardless of the machine learning algorithm, these systems often fail in discriminating AMPs from their shuffled versions, leading to the need for new training methods to overcome this bias. Aiming to solve this bias, here we present "Sense the Moment", a prediction system capable of discriminating AMPs and shuffled versions., Methods: The system was trained using 776 entries: 388 from known AMPs and another 388 based on shuffled versions of known AMPs. Each entry contained the geometric average of three hydrophobic moments measured with different scales., Results: The model showed good accuracy (>80%) and excellent sensitivity (>90%) for AMP prediction, exceeding deep-learning-based methods., Conclusion: Our results demonstrate the system's applicability, aiding in identifying and discarding non-AMPs, since the number of false negatives is lower than false positives., General Significance: The application of this model in virtual screening protocols for identifying and/or creating antimicrobial agents could aid in the identification of potential drugs to control pathogenic microorganisms and in solving the antibiotic resistance crisis., Availability: The system was implemented as a web application, available at ., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Antimicrobial peptides (AMPs) are molecules with potential application for the treatment of microorganism infections. We, herein, describe the structure, activity, and mechanism of action of RQ18, an α-helical AMP that displays antimicrobial activity against Gram-positive and Gram-negative bacteria, and yeasts from the Candida genus., Methods: A physicochemical-guided design assisted by computer tools was used to obtain our lead peptide candidate, named RQ18. This peptide was assayed against Gram-positive and Gram-negative bacteria, yeasts, and mammalian cells to determine its selectivity index. The secondary structure and the mechanism of action of RQ18 were investigated using circular dichroism, large unilamellar vesicles, and molecular dynamic simulations., Results: RQ18 was not cytotoxic to human lung fibroblasts, peripheral blood mononuclear cells, red blood cells, or Vero cells at MIC values, exhibiting a high selectivity index. Circular dichroism analysis and molecular dynamic simulations revealed that RQ18 presents varying structural profiles in aqueous solution, TFE/water mixtures, SDS micelles, and lipid bilayers. The peptide was virtually unable to release carboxyfluorescein from large unilamellar vesicles composed of POPC/cholesterol, model that mimics the eukaryotic membrane, indicating that vesicles' net charges and the presence of cholesterol may be related with RQ18 selectivity for bacterial and fungal cell surfaces., Conclusions: RQ18 was characterized as a membrane-active peptide with dual antibacterial and antifungal activities, without compromising mammalian cells viability, thus reinforcing its therapeutic application., General Significance: These results provide further insight into the complex process of AMPs interaction with biological membranes, in special with systems that mimic prokaryotic and eukaryotic cell surfaces., (Published by Elsevier B.V.)

Background: Amylin is a 37-amino-acid peptide hormone co-secreted with insulin, which participates in glucose homeostasis. This hormone is able to aggregate in a β-sheet conformation and deposit in islet amyloids, a hallmark in type II diabetes. Since amylin is a gene-encoded hormone, this peptide has variants caused by point mutations that can impact its functions., Methods: Here, we analyzed the structural effects caused by S20G and G33R point mutations which, according to the 1000 Genomes Project, have frequency in East Asian and European populations, respectively. The analyses were performed by means of aggrescan server, SNP functional effect predictors, and molecular dynamics., Results: We found that both mutations have aggregation potential and cause changes in the monomeric forms when compared with wild-type amylin. Furthermore, comparative analyses with pramlintide, an amylin drug analogue, allowed us to infer that second α-helix maintenance may be related to the aggregation potential., Conclusions: The S20G mutation has been described as pathologically related, which is in agreement with our findings. In addition, our data suggest that the G33R mutation might have a deleterious effect. The data presented here also provide new therapy opportunities, whether for creating more effective drugs for diabetes or implementing specific treatment for patients with these mutations., General Significance: Our data could help to better understand the impact of mutations on the wild-type amylin sequence, as a starting point for the evaluation and characterization of other variations. Moreover, these findings could improve the health of patients with type II diabetes., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Bacterial infections represent a major worldwide health problem the antimicrobial peptides (AMPs) have been considered as potential alternative agents for treating these infections. Here we demonstrated the antimicrobial activity of EcDBS1R6, a peptide derived from a signal peptide sequence of Escherichia coli that we previously turned into an AMP by making changes through the Joker algorithm., Methods: Antimicrobial activity was measured by broth microdilution method. Membrane integrity was measured using fluorescent probes and through scanning electron microscopy imaging. A sliding window of truncated peptides was used to determine the EcDBS1R6 active core. Molecular dynamics in TFE/water environment was used to assess the EcDBS1R6 structure., Results: Signal peptides are known to naturally interact with membranes; however, the modifications introduced by Joker transformed this peptide into a membrane-active agent capable of killing bacteria. The C-terminus was unable to fold into an α-helix whereas its fragments showed poor or no antimicrobial activity, suggesting that the EcDBS1R6 antibacterial core was located at the helical N-terminus, corresponding to the signal peptide portion of the parent peptide., Conclusion: The strategy of transforming signal peptides into AMPs appears to be promising and could be used to produce novel antimicrobial agents., General Significance: The process of transforming an inactive signal peptide into an antimicrobial peptide could open a new venue for creating new AMPs derived from signal peptides., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Innovative alternatives to control bacterial infections are need due to bacterial resistance rise. Antimicrobial peptides (AMPs) have been considered as the new generation of antimicrobial agents. Based on the fact that AMPs are sequence-dependent, a linguistic model for designing AMPs was previously developed, considering AMPs as a formal language with a grammar (patterns or motifs) and a vocabulary (amino acids). Albeit promising, that model has been poorly exploited mainly because thousands of sequences need to be generated, and the outcome has high similarity to already known AMPs. Here we present Joker, an innovative algorithm that improves the application of the linguistic model for rational design of antimicrobial peptides. We modelled the AMPs as a card game, where Joker combines the cards in the hand (patterns) with the cards in the table (sequence templates), generating a few variants. Our algorithm is capable of improving existing AMPs or even creating new AMPs from inactive peptides. A standalone version of Joker is available for download at and requires a Linux 32-bit machine., (Copyright © 2018 Elsevier B.V. All rights reserved.)